In nature, plants are simultaneously exposed to a combination of biotic and abiotic stresses that limit crop yields. Only recently, researchers have started understanding the molecular basis of combined biotic and abiotic stress interactions. Evidences suggest that under combined stress plants exhibit tailored physiological and molecular responses, in addition to several shared responses as part of their stress tolerance strategy. These tailored responses are suggested to occur only in plants exposed to simultaneous stresses and this information cannot be inferred from individual stress studies. In this review article, we provide update on the responses of plants to simultaneous biotic and abiotic stresses, in particular drought and pathogen. Simultaneous occurrence of drought and pathogen during plant growth provokes complex pathways controlled by different signaling events resulting in positive or negative impact of one stress over the other. Here, we summarize the effect of combined drought and pathogen infection on plants and highlight the tailored strategies adapted by plants. Besides, we enumerate the evidences from pathogen derived elicitors and ABA response studies for understanding simultaneous drought and pathogen tolerance.

In field conditions, plants are often simultaneously exposed to multiple biotic and abiotic stresses resulting in substantial yield loss. Plants have evolved various physiological and molecular adaptations to protect themselves under stress combinations. Emerging evidences suggest that plant responses to a combination of stresses are unique from individual stress responses. In addition, plants exhibit shared responses which are common to individual stresses and stress combination. In this review, we provide an update on the current understanding of both unique and shared responses. Specific focus of this review is on heat-drought stress as a major abiotic stress combination and, drought-pathogen and heat-pathogen as examples of abiotic-biotic stress combinations. We also comprehend the current understanding of molecular mechanisms of cross talk in relation to shared and unique molecular responses for plant survival under stress combinations. Thus, the knowledge of shared responses of plants from individual stress studies and stress combinations can be utilized to develop varieties with broad spectrum stress tolerance.

Acclimation of plants with an abiotic stress can impart tolerance to some biotic stresses. Such a priming response has not been widely studied. In particular, little is known about enhanced defense capacity of drought stress acclimated plants to fungal and bacterial pathogens. Here we show that prior drought acclimation in Nicotiana benthamiana plants imparts tolerance to necrotrophic fungus, Sclerotinia sclerotiorum, and also to hemi-biotrophic bacterial pathogen, Pseudomonas syringae pv. tabaci. S. sclerotiorum inoculation on N. benthamiana plants acclimated with drought stress lead to less disease-induced cell death compared to non-acclimated plants. Furthermore, inoculation of P. syringae pv. tabaci on N. benthamiana plants acclimated to moderate drought stress showed reduced disease symptoms. The levels of reactive oxygen species (ROS) in drought acclimated plants were highly correlated with disease resistance. Further, in planta growth of GFPuv expressing P. syringae pv. tabaci on plants pre-treated with methyl viologen showed complete inhibition of bacterial growth. Taken together, these experimental results suggested a role for ROS generated during drought acclimation in imparting tolerance against S. sclerotiorum and P. syringae pv. tabaci. We speculate that the generation of ROS during drought acclimation primed a defense response in plants that subsequently caused the tolerance against the pathogens tested.

Background: Understanding the function of a particular gene under various stresses is important for engineering plants for broad-spectrum stress tolerance. Although virus-induced gene silencing (VIGS) has been used to characterize genes involved in abiotic stress tolerance, currently available gene silencing and stress imposition methodology at the whole plant level is not suitable for high-throughput functional analyses of genes. This demands a robust and reliable methodology for characterizing genes involved in abiotic and multi-stress tolerance. Results: Our methodology employs VIGS-based gene silencing in leaf disks combined with simple stress imposition and effect quantification methodologies for easy and faster characterization of genes involved in abiotic and multi-stress tolerance. By subjecting leaf disks from gene-silenced plants to various abiotic stresses and inoculating silenced plants with various pathogens, we show the involvement of several genes for multi-stress tolerance. In addition, we demonstrate that VIGS can be used to characterize genes involved in thermotolerance. Our results also showed the functional relevance of NtEDS1 in abiotic stress, NbRBX1 and NbCTR1 in oxidative stress; NtRAR1 and NtNPR1 in salinity stress; NbSOS1 and NbHSP101 in biotic stress; and NtEDS1, NbETR1, NbWRKY2 and NbMYC2 in thermotolerance. Conclusions: In addition to widening the application of VIGS, we developed a robust, easy and high-throughput methodology for functional characterization of genes involved in multi-stress tolerance.

Transcription factors (TFs) play a central role in regulating molecular level responses of plants to external stresses such as water limiting conditions, but identification of such TFs in the genome remains a challenge. Here, we describe a network-based supervised machine learning framework that accurately predicts and ranks all TFs in the genome according to their potential association with drought tolerance. We show that top ranked regulators fall mainly into two ‘age’ groups; genes that appeared first in land plants and genes that emerged later in the Oryza clade. TFs predicted to be high in the ranking belong to specific gene families, have relatively simple intron/exon and protein structures, and functionally converge to regulate primary and secondary metabolism pathways. Repeated trials of nested cross-validation tests showed that models trained only on regulatory network patterns, inferred from large transcriptome datasets, outperform models trained on heterogenous genomic features in the prediction of known drought response regulators. A new R/Shiny based web application, called the DroughtApp, provides a primer for generation of new testable hypotheses related to regulation of drought stress response. Furthermore, to test the system we experimentally validated predictions on the functional role of the rice transcription factor OsbHLH148 , using RNA sequencing of knockout mutants in response to drought stress and protein-DNA interaction assays. Our study exemplifies the integration of domain knowledge for prioritization of regulatory genes in biological pathways of well-studied agricultural traits. One Sentence Summary: Network-based supervised machine learning accurately predicts transcription factors involved in drought tolerance.